A traditional task-oriented conversation system includes a speech recognition module, a natural language interpreting module, a conversation management module, a back-end data processing module, a natural language generating module and a speech synthesis module. When a specific task-oriented conversation system is constructed, four modules, except the speech recognition module and the speech synthesis module, need customized development based on the specific application task.
Regarding the customized development of tasks, there are mainly two approaches at present:
1. Each module is redeveloped according to the service logic of the specific task. The system customized using this approach can generally meet the requirement of the specific task well, but the effort on the development is large.
2. The conversation system state expression and the system action are abstracted, and a learning reinforcement method is used to construct a conversation system via environmental interaction learning. The system customized using this approach needs a large amount of conversation materials to perfect a conversation strategy and is also relatively difficult to implement, the understandability of the learnt strategy is poor, and the controllability of the system is low.